Ihre KI-Agenten vergessen alles zwischen den Sitzungen. Connapse behebt das.
Jedes Mal, wenn Sie eine neue Konversation starten, beginnt Ihr KI-Agent bei Null – ohne Erinnerung an frühere Recherchen, ohne Zugriff auf Ihre Dokumente, ohne angesammeltes Wissen. Connapse ist ein Open-Source-Wissens-Backend, das Agenten ein dauerhaftes, durchsuchbares Gedächtnis verleiht. Laden Sie Dokumente hoch oder verweisen Sie auf Ihre bestehenden Amazon S3-Buckets, Azure Blob Storage-Container oder lokale Dateisysteme. Agenten können über 11 MCP-Tools, REST-API oder CLI ihren eigenen Recherche-Korpus abfragen und aufbauen. Container-isoliert, hybride Suche (Vektor + Stichwort), selbstgehostet und privat. Bereitstellung in 60 Sekunden mit Docker. Aufgebaut auf .NET 10.
KI-Agenten fragen Ihre Wissensdatenbank über den MCP-Server ab und erhalten strukturierte Antworten mit Quellenangaben aus Ihren Dokumenten.
Wechseln Sie Embedding-Anbieter, passen Sie Chunking-Parameter an und konfigurieren Sie die Suche – alles zur Laufzeit, ohne Neustart.
git clone https://github.com/Destrayon/Connapse.git && cd Connapse && docker-compose up -d
# Open http://localhost:5001.NET 10 SDK (für die Entwicklung)
(Optional) Ollama für lokale Embeddings
# Clone the repository
git clone https://github.com/Destrayon/Connapse.git
cd Connapse
# Set required auth environment variables (or use a .env file)
export CONNAPSE_ADMIN_EMAIL=admin@example.com
export CONNAPSE_ADMIN_PASSWORD=YourSecurePassword123!
export Identity__Jwt__Secret=$(openssl rand -base64 64)
# Start all services (PostgreSQL, MinIO, Web App)
docker-compose up -d
# Open http://localhost:5001 — log in with the admin credentials aboveDer erste Start wird:
Docker-Images herunterladen (~2-5 Minuten)
PostgreSQL mit pgvector-Erweiterung initialisieren und EF Core-Migrationen ausführen
MinIO-Buckets erstellen
Das Administratorkonto (aus Umgebungsvariablen) anlegen und die Webanwendung starten
# Start infrastructure only (database + object storage)
docker-compose up -d postgres minio
# Run the web app locally
dotnet run --project src/Connapse.Web
# Run all tests
dotnet test
# Run just unit tests
dotnet test --filter "Category=Unit"Installieren Sie die CLI (wählen Sie eine Option):
# Option A: .NET Global Tool (requires .NET 10)
dotnet tool install -g Connapse.CLI
# Option B: Download native binary from GitHub Releases (no .NET required)
# https://github.com/Destrayon/Connapse/releasesGrundlegende Verwendung:
# Authenticate first
connapse auth login --url https://localhost:5001
# Create a container (project)
connapse container create my-project --description "My knowledge base"
# Upload files
connapse upload ./documents --container my-project
# Search
connapse search "your query" --container my-project
# Update to latest release (--pre to include alpha/pre-release builds)
connapse update
connapse update --preConnapse enthält einen Model Context Protocol (MCP)-Server zur Integration mit Claude und jedem MCP-Client.
Einrichtung: Erstellen Sie einen Agenten-API-Schlüssel über die Web-UI (Einstellungen → Agenten-API-Schlüssel) oder CLI (connapse auth agent-key create) und fügen Sie dann den Konfigurations-Snippet für Ihren Client hinzu:
claude mcp add connapse --transport streamable-http http://localhost:5001/mcp --header "X-Agent-Api-Key: YOUR_API_KEY"Fügen Sie dies zu Ihrer claude_desktop_config.json hinzu:
{
"mcpServers": {
"connapse": {
"transport": "streamable-http",
"url": "http://localhost:5001/mcp",
"headers": {
"X-Agent-Api-Key": "YOUR_API_KEY"
}
}
}
}Fügen Sie dies zu Ihrer .vscode/settings.json (VS Code) oder Cursor MCP-Konfiguration hinzu:
{
"mcp": {
"servers": {
"connapse": {
"transport": "streamable-http",
"url": "http://localhost:5001/mcp",
"headers": {
"X-Agent-Api-Key": "${input:connapseApiKey}"
}
}
}
}
}VS Code wird bei der ersten Verwendung nach dem API-Schlüssel fragen.
Der MCP-Server stellt 11 Tools bereit:
Tool | Beschreibung |
| Erstellt einen neuen Container zur Dateiorganisation |
| Listet alle Container mit Dokumentenanzahl auf |
| Löscht einen Container |
| Ruft Container-Statistiken ab (Dokumente, Chunks, Speicher, Embeddings) |
| Lädt eine einzelne Datei in einen Container hoch |
| Lädt bis zu 100 Dateien in einem Vorgang hoch |
| Listet Dateien und Ordner in einem Pfad auf |
| Ruft den vollständigen geparsten Textinhalt eines Dokuments ab |
| Löscht eine einzelne Datei aus einem Container |
| Löscht bis zu 100 Dateien in einem Vorgang |
| Semantische, Stichwort- oder hybride Suche innerhalb eines Containers |
Vollständige Referenz: Siehe docs/mcp-tools.md für Parametertabellen, Rückgabeformate, Fehlerfälle und Anwendungsbeispiele.
Schreibschutz: Amazon S3- und Azure Blob Storage-Container sind schreibgeschützt (synchronisiert von der Quelle). Dateisystem-Container respektieren Berechtigungs-Flags pro Container. Upload- und Lösch-Tools geben einen Fehler für Container zurück, die Schreibzugriffe blockieren.
"Erstelle einen Container namens 'Projekt-Recherche' für meine Architektur-Notizen"
"Lade alle PDFs in meinem Downloads-Ordner in den Projekt-Recherche-Container hoch"
"Durchsuche meinen Projekt-Recherche-Container nach Informationen über Ratenbegrenzungsstrategien"
"Liste alle Dateien im Ordner /notizen/ meines Projekt-Recherche-Containers auf"
"Rufe den vollständigen Text von distributed-systems-notes.md aus Projekt-Recherche ab"
"Lösche meeting-2026-03-14.md aus Projekt-Recherche und lade diese aktualisierte Version hoch"
"Lösche alle Dateien im Ordner /entwürfe/ von Projekt-Recherche"
"Wie viele Dokumente und Chunks befinden sich in meinem Projekt-Recherche-Container?"
Verbindung verweigert auf localhost:5001 — Docker läuft nicht oder Port-Konflikt. Prüfen Sie docker compose ps und docker compose logs web.
401 Nicht autorisiert / API-Schlüssel funktioniert nicht — Überprüfen Sie den Schlüssel unter Einstellungen > Agenten-API-Schlüssel. Schlüssel werden nur einmal bei der Erstellung angezeigt.
Tools erscheinen nicht in Claude — Starten Sie Ihren MCP-Client nach Konfigurationsänderungen neu. Überprüfen Sie den Endpunkt mit curl http://localhost:5001/mcp.
Uploads schlagen fehl oder Zeitüberschreitung — Prüfen Sie, ob der Dateityp in der Zulassungsliste steht. Die maximale Dateigröße hängt von der Serverkonfiguration ab.
Suche liefert keine Ergebnisse — Dokumente benötigen nach dem Hochladen Zeit für das Embedding. Prüfen Sie die Container-Statistiken auf den Embedding-Fortschritt.
Related MCP server: mnemostack
🗂️ Container-isoliertes Wissen — Jedes Projekt erhält seinen eigenen Vektor-Index, Speicher-Konnektor und Suchkonfiguration. Keine Kreuzkontamination zwischen Projekten, Teams oder Kunden.
🔍 Hybride Suche — Vektorähnlichkeit + Volltext-Stichwortsuche mit konfigurierbarer Fusion (konvexe Kombination, DBSF, AutoCut). Erhalten Sie Ergebnisse, die eine reine Vektorsuche übersieht.
🧠 Multi-Provider-KI — Wechseln Sie zwischen Ollama, OpenAI, Azure OpenAI und Anthropic für Embeddings und LLM – zur Laufzeit, pro Container, ohne Neustart.
🔌 Indizieren Sie Ihren bestehenden Speicher — Verbinden Sie MinIO, lokales Dateisystem (Live-Dateiüberwachung), Amazon S3 (IAM-Auth) oder Azure Blob Storage (verwaltete Identität). Ihre Dateien bleiben, wo sie sind.
🤖 4 Zugriffsoberflächen — Web-UI, REST-API, CLI (native Binärdateien) und MCP-Server (11 Tools für Claude). Entwickelt für Menschen, Skripte und KI-Agenten gleichermaßen.
🔐 Enterprise-Authentifizierung — Mehrstufiges RBAC (Cookie + OAuth 2.1 + PAT + JWT) mit AWS IAM Identity Center und Azure AD-Identitätsverknüpfung. Cloud-Berechtigungen sind die Quelle der Wahrheit.
🐳 Ein-Befehl-Bereitstellung — Docker Compose mit PostgreSQL + pgvector, MinIO und optional Ollama. Strukturiertes Audit-Logging und Ratenbegrenzung integriert.
📄 Multi-Format-Aufnahme: PDF, Office-Dokumente, Markdown, Klartext – automatisch geparst, gechunked und eingebettet
⚡ Echtzeitverarbeitung: Hintergrundaufnahme mit Live-Fortschritts-Updates via SignalR
🎛️ Laufzeitkonfiguration: Ändern Sie Chunking-Strategie, Embedding-Modell und Sucheinstellungen pro Container ohne Neustart
☁️ Cloud-Identitätsverknüpfung: AWS IAM Identity Center (Geräte-Auth-Flow) + Azure AD (OAuth2+PKCE) mit IAM-abgeleiteter Bereichsdurchsetzung
👥 Einladungsbasierter Zugriff: Admin-gesteuerte Benutzerregistrierung mit vier Rollen (Admin / Editor / Viewer / Agent)
🤖 Agentenverwaltung: Dedizierte Agenten-Entitäten mit API-Schlüssel-Lebenszyklus, Bereichsberechtigungen und Audit-Trails
📋 Audit-Logging: Strukturierter Audit-Trail für Uploads, Löschvorgänge, Container-Operationen und Auth-Ereignisse
📦 CLI-Distribution: Native, in sich geschlossene Binärdateien (Windows/Linux/macOS) und .NET-Global-Tool via NuGet
🔄 Cross-Modell-Suche: Wechseln Sie Embedding-Modelle mitten im Projekt – automatischer semantischer→hybrider Fallback für Legacy-Vektoren
KI-Agenten-Entwickler, die ein Wissens-Backend benötigen, das ihre Agenten sowohl abfragen als auch aufbauen können – Forschung hochladen, einen Korpus kuratieren und ihn via MCP oder REST-API durchsuchen
.NET / Azure-Teams, die eine RAG-Plattform suchen, die zu ihrem bestehenden Stack und ihrer Cloud-Identität passt
Enterprise-Teams, die projektisolierte Wissensdatenbanken mit korrektem RBAC und Audit-Trails benötigen
Jeden, der es leid ist, Dateien erneut hochzuladen – verweisen Sie Connapse auf Ihren bestehenden Amazon S3/Azure Blob Storage/Dateisystem-Speicher
Dieses Projekt befindet sich in aktiver Entwicklung (v0.3.2) und nähert sich der Produktionsreife.
v0.3.x fügt eine Cloud-Konnektor-Architektur mit IAM-basierter Zugriffskontrolle, Multi-Provider-Embeddings und LLM-Unterstützung, Cloud-Identitätsverknüpfung (AWS SSO + Azure AD) und Ratenbegrenzung hinzu.
✅ Authentifizierung und Autorisierung (v0.2.0)
✅ Rollenbasierte Zugriffskontrolle (Admin / Editor / Viewer / Agent)
✅ Audit-Logging
✅ Cloud-Identitätsverknüpfung — AWS IAM Identity Center + Azure AD OAuth2+PKCE (v0.3.0)
✅ IAM-abgeleitete Bereichsdurchsetzung — Cloud-Berechtigungen sind die Quelle der Wahrheit (v0.3.0)
✅ Ratenbegrenzung — integrierte ASP.NET Core-Middleware mit Richtlinien pro Benutzer und pro IP (v0.3.2)
⚠️ Setzen Sie ein starkes Identity__Jwt__Secret in der Produktion — siehe Bereitstellungsanleitung
Siehe SECURITY.md für die vollständige Sicherheitsrichtlinie.
┌──────────────────────────────────────────────────────────────────────┐
│ Access Surfaces │
│ Web UI (Blazor) │ REST API │ CLI │ MCP Server │
└─────────────┬────────────────────────────────────────────────────────┘
│
┌─────────────▼────────────────────────────────────────────────────────┐
│ Core Services Layer │
│ Document Store │ Vector Store │ Search │ Ingestion │
└─────────────┬────────────────────────────────────────────────────────┘
│
┌─────────────▼────────────────────────────────────────────────────────┐
│ Connectors Layer │
│ MinIO │ Filesystem │ Amazon S3 │ Azure Blob Storage │
└─────────────┬────────────────────────────────────────────────────────┘
│
┌─────────────▼────────────────────────────────────────────────────────┐
│ Infrastructure │
│ PostgreSQL+pgvector │ MinIO (S3) │ Ollama (optional) │
└──────────────────────────────────────────────────────────────────────┘G
Delete up to 100 files in one call. Returns per-file success/failure results.
| Name | Required | Description | Default |
|---|---|---|---|
| containerId | Yes | Container ID or name | |
| fileIds | Yes | JSON array of file (document) IDs to delete, e.g. ["id1","id2"]. Max 100. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds valuable behavioral details beyond the destructiveHint annotation, such as the batch limit and per-file result format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose and limits, no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, description compensates by noting per-file results. Covers key constraints (limit, return format). Could mention error handling for missing files, but sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema already describes both parameters fully (100% coverage). The description does not add new semantic value beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (delete), resource (files), and limits (up to 100, one call), distinguishing it from single-file deletion and bulk upload.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage for deleting multiple files up to 100, but lacks explicit when-to-use vs alternatives or when-not-to-use scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Upload up to 100 files in one call. Each file is parsed, chunked, and embedded. Returns per-file results.
| Name | Required | Description | Default |
|---|---|---|---|
| containerId | Yes | Container ID or name | |
| files | Yes | JSON array of file objects. Each object: {"filename":"name.txt", "content":"...", "encoding":"text|base64", "folderPath":"/optional/"}. Max 100. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the processing pipeline (parse, chunk, embed) but lacks details on error handling for partial failures, file size limits, or supported formats. Adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler. Every sentence provides essential information: capacity, processing, and return type.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so description should clarify return values. 'Returns per-file results' is vague; could specify status, errors, or IDs. Also missing constraints like file size, timeout, or authentication requirements.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, both parameters are documented. The description adds 'Max 100' which reinforces the schema's 'max 100' constraint in files parameter description, but no new meaning beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verb 'Upload' with resource 'files', states the limit 'up to 100', and explains the processing pipeline ('parsed, chunked, and embedded'). It clearly distinguishes from sibling 'upload_file' which handles single files.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies bulk usage via 'up to 100 files' but does not explicitly state when to use this over single upload or alternatives like 'search_knowledge'. No when-not or alternative tool names are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a new container for organizing documents. Use when setting up a new knowledge domain or project.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Container name (lowercase alphanumeric and hyphens, 2-128 chars) | |
| description | No | Optional description for the container |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It only states creation purpose, lacking details on permissions, side effects, or duplicate handling. Adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two clear sentences, front-loaded with action, no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, and description does not mention return value (e.g., created container info). Adequate for simple tool but incomplete on expected response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage with descriptions for both parameters. Description repeats the same meaning, adding no extra value. Baseline 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states action (create), resource (container), and purpose (organizing documents). Distinguishes from sibling tools like container_delete or container_list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use when setting up a new knowledge domain or project', providing clear context. Does not mention exclusions or alternatives, but context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete a container. MinIO containers must be emptied first. Filesystem/S3/Azure files are not deleted — only the index is removed.
| Name | Required | Description | Default |
|---|---|---|---|
| containerId | Yes | Container ID or name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the destructiveHint annotation, the description adds important behavioral details: files are not deleted, only the index is removed, and MinIO containers must be emptied first.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with the action and followed by key constraints, with no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description covers the essential behavior, prerequisites, and what is not deleted, though it could mention irreversibility.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description does not add additional meaning beyond the schema's parameter description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool deletes a container, and differentiates from sibling tools like delete_file by clarifying that files are not deleted, only the index.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It specifies a prerequisite (MinIO containers must be emptied) and clarifies what is not affected, but does not explicitly compare to alternatives like bulk_delete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all containers with document counts. Use to discover available knowledge bases before searching.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the description doesn't need to repeat these. It adds the return of document counts, but lacks details on pagination or performance. With annotations covering safety, this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences (12 words) that are front-loaded and contain no fluff. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and no output schema, the description covers the tool's purpose and usage context well. It mentions 'document counts,' giving insight into return data, but could be slightly more detailed on output format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so schema coverage is 100%. The description adds no parameter info, which is appropriate. Baseline 4 for 0 parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists all containers with document counts and specifies its use for discovering knowledge bases before searching. This differentiates it from sibling tools like container_stats or container_create.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use to discover available knowledge bases before searching,' providing clear context for when to use it. It lacks explicit exclusion guidance but is sufficient given the sibling context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get container statistics: document counts, chunk count, storage size, and embedding model info.
| Name | Required | Description | Default |
|---|---|---|---|
| containerId | Yes | Container ID or name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent behavior. The description adds value by specifying the exact statistics returned, providing context beyond what annotations offer. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently conveys the tool's purpose and output details with no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple, read-only tool with one parameter and no output schema, the description fully covers what the tool returns and its use case, making it complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides full coverage for the single parameter (containerId) with a clear description. The tool description does not add additional semantic meaning for the parameter, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get container statistics' and lists specific data (document counts, chunk count, storage size, embedding model info), making it distinct from siblings like container_list which only list container IDs or names.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for retrieving statistics of a specific container but does not explicitly state when to use it over alternatives or provide exclusions. No guidance on prerequisites (e.g., container must exist).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete a file and all its chunks and vectors. To update a file, delete it first then re-upload with upload_file.
| Name | Required | Description | Default |
|---|---|---|---|
| containerId | Yes | Container ID or name | |
| fileId | Yes | File (document) ID to delete |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already flag destructiveHint=true. The description adds value by specifying that chunks and vectors are also deleted, giving a fuller scope of the operation's effects. It does not contradict annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: first clearly states the purpose, second provides a practical usage tip. No fluff, front-loaded, every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple destructive tool with full schema coverage and an annotation, the description adequately covers the deletion scope and provides an update workflow hint. It could mention irreversibility or permissions, but these are implied by the destructive hint. Overall sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already documents both parameters (containerId and fileId) with descriptions, achieving 100% coverage. The description does not add extra semantic detail beyond what the schema provides, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Delete a file and all its chunks and vectors', which specifies the verb (delete) and resource (file and associated data). It also distinguishes from siblings like bulk_delete by implying single-file deletion via the fileId parameter, and provides additional context about updating.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes explicit guidance for updating: 'To update a file, delete it first then re-upload with upload_file.' However, it does not explicitly warn against using this tool for bulk deletions or when to prefer bulk_delete, though the parameterization hints at single-file usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve a document's full text by ID or path. Returns extracted text for binary formats (PDF, DOCX, PPTX).
| Name | Required | Description | Default |
|---|---|---|---|
| containerId | Yes | Container ID or name | |
| fileId | Yes | Document ID (UUID) or virtual path (e.g., '/docs/readme.md') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and idempotentHint. Description adds that it returns extracted text for binary formats (PDF, DOCX, PPTX) beyond what annotations convey.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two front-loaded sentences, no waste, clearly communicate purpose and key behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple retrieval tool with 2 required parameters, description covers purpose, return value (full text, extraction for binaries). Lacks error handling or response format, but sufficient for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with good descriptions for both parameters. Description restates 'by ID or path' but adds no new semantic detail beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'Retrieve' and resource 'document's full text', specifies query by ID or path, and mentions handling of binary formats. Distinguishes from siblings like list_files (metadata) and search_knowledge.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied usage context as a read-only retrieval tool, but no explicit when-to-use or alternatives (e.g., 'for metadata use list_files'). Clear enough for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List files and folders at a path within a container. Use to browse container contents before retrieving documents.
| Name | Required | Description | Default |
|---|---|---|---|
| containerId | Yes | Container ID or name | |
| path | No | Folder path to list (default: root '/') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds the browsing context but does not disclose additional behavioral traits such as pagination or error handling, which would provide further transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences: one for the core function and one for usage guidance. Every word is necessary and no information is redundant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list operation with two parameters and no output schema, the description is fairly complete. It covers what the tool does and when to use it, though it could mention the return structure (e.g., list of file names) to be fully self-contained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add any parameter details beyond what the schema already provides for containerId and path.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'list' and specifies the resource 'files and folders at a path within a container'. It also provides context by stating its use case 'before retrieving documents', which distinguishes it from siblings like get_document, upload_file, and delete_file.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises using this tool to browse container contents before retrieving documents, giving clear context. However, it does not explicitly exclude other uses or mention alternatives beyond the implied contrast with get_document.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search a container using semantic, keyword, or hybrid mode. Returns ranked document chunks with scores. Use when answering questions from stored knowledge.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query text | |
| containerId | Yes | Container ID or name to search within | |
| mode | No | Search mode: Semantic (vector), Keyword (full-text), or Hybrid (both). Default: Hybrid | |
| topK | No | Number of results to return. Default: 10 | |
| path | No | Optional: Filter results to a folder subtree (e.g., '/docs/') | |
| minScore | No | Minimum similarity score floor (0.0-1.0). Defaults to 0.05. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the main behavioral traits are covered. The description adds 'Returns ranked document chunks with scores,' which gives some extra context but is not critical. No destructive behavior or auth needs are mentioned, but annotations suffice.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, with the first sentence covering purpose and behavior, and the second giving usage guidance. Every sentence adds value, and there is no unnecessary text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the 100% schema coverage, the presence of annotations, and no output schema, the description is fairly complete. It covers purpose, usage, and return type. However, it could briefly mention that results are from the specified container, but that is implicit in the first sentence.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with each parameter having a description in the input schema. The tool description does not add any additional information about the parameters beyond what the schema already provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search a container') and specifies the modes (semantic, keyword, hybrid). It also mentions the return type ('ranked document chunks with scores'), making the tool's purpose distinct from siblings like container_list or get_document.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly suggests using the tool 'when answering questions from stored knowledge,' providing a clear use case. However, it does not mention when not to use it or compare to alternatives, which would make it a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Upload a file to be parsed, chunked, embedded, and made searchable. Provide either 'content' (base64) or 'textContent' (raw text), not both.
| Name | Required | Description | Default |
|---|---|---|---|
| containerId | Yes | Container ID or name | |
| content | No | Base64-encoded file content. For binary files (PDF, DOCX, images). Mutually exclusive with textContent. | |
| textContent | No | Raw text content for text-based files (Markdown, TXT, CSV, JSON, etc.). Mutually exclusive with content. | |
| fileName | No | Original file name with extension | |
| path | No | Destination folder path (e.g., '/docs/2026/') | |
| strategy | No | Chunking strategy: Semantic, FixedSize, or Recursive. Default: Semantic |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It mentions the processing pipeline (parse, chunk, embed) and mutual exclusivity of content types, but does not disclose failure modes, idempotency, permissions, or response format. This is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no waste. The purpose is front-loaded, and essential usage hint is included. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 6 parameters (1 required), schema covers all, and no output schema, the description is fairly complete. It explains the overall goal and key constraint. However, it omits details on return value or confirmation. Still, it is adequate for an agent to understand and use the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description reiterates the mutual exclusivity of content and textContent already in schema descriptions, adding minimal extra meaning. No new parameter semantics beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Upload a file') and the resource, and describes the processing pipeline ('parsed, chunked, embedded, and made searchable'). This distinguishes it from sibling tools like bulk_upload (multiple files) and delete_file (deletion).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a parameter usage guideline ('Provide either content or textContent, not both') but lacks explicit guidance on when to use this tool over siblings like bulk_upload or search_knowledge. No when-not-to-use or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.3.2bulk_deletebulk_uploadcontainer_createcontainer_deletecontainer_listcontainer_statsdelete_fileget_documentlist_filessearch_knowledgeupload_fileScored across 11 tools
Each tool targets a distinct operation: container management, file operations, bulk actions, and search. No two tools have overlapping purposes; even delete_file and bulk_delete are clearly scoped by single vs batch.
All tools follow a consistent verb_noun snake_case pattern (e.g., container_create, upload_file, search_knowledge). No mixed conventions or vague verbs.
11 tools is well-scoped for a knowledge management server, covering container CRUD, file operations, bulk operations, and search. No redundancy or missing essential operations.
Covers most lifecycle operations: container create/list/delete, file upload/get/list/delete, bulk upload/delete, and search. Missing container update and folder creation, but these are minor gaps that agents can work around.
Cloud or self-hosted knowledge for AI agents: hybrid search, reranking, GraphRAG, scoped MCP tools.
Multi-engine search for AI agents. Trust scoring, local corpus, MCP-native. Self-hostable, BYOK.
Governed, auditable knowledge your team curates for its AI assistants, self-hostable
MCP-first toolbox for agents: KV storage, auth, queue, and utility tools. Free in early access.
